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Conduct the Cluster Detection Based on Events Using the Approximate Normal Approach
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DESCRIPTION: 

Conduct the cluster detection tests based on events using a normal approximation (Torabi and Rosychuk, Submitted).


USAGE: 

normalevent(cases,events,population,nnmat,cov,signiflevel=0.05)


REQUIRED ARGUMENTS: 

cases 
     this is a vector where each element corresponds to the number of cases for each cell and
     has dimension equals to the total number of cells. 
     
events 
     this is a vector where each element corresponds to the number of events for each cell and
     has dimension equals to the total number of cells.
 
population 
     this is a vector where each element corresponds to the population size for each cell and 
     has dimension equals to the total number of cells.

nnmat 
     the square matrix containing the ordered nearest neighbours: nnmat[i,1]=i, nnmat[i,j]=
     the (j+1)th nearest neighbour to cell i. Note that the cells are assumed to be labelled
     from 1 to the total number of cells.

cov
     the matrix of contatining the details of the strata as well as cells and events. 
     In this matrix, for each cell we have the number of events for each interaction between 
     strata. For example, if we have two strata gender (0,1) and age (0-17), we need to have
     the number of events for (gender=0,age=0,cell=1), (gender=0,age=1,cell=1),,...,
     (gender=1,age=17,cell=m). Assume the first column of the matrix is the cell number, 
     second column is the number of events, the third column is the first strata, the fourth column 
     is the second strata and so on. 

signiflevel
     the level of significance for each cluster test. The default is 0.05. 


VALUE: 

a matrix containing the results of the testing cluster detection. The columns are the cell number 
(Cell), the number of neighbours (W), the cluster size tests (ClustSize), the observed value of 
the test statistic (TestStat), the observed number of events in the cell and its neighbours (Event),
the number of population size in the cell and its neighbours (NNO.cell), the p-value for the test (p-value).